• Proceedings of the Computational Structural Engineering Institute Conference
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• 2006.04a
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• pp.786-793
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• 2006

For the purpose of developing a vibration-based tension force evaluation procedure for hangers in suspension bridges, a 3D finite element model of hangers is constructed in this paper. With the developed finite element formulation, a frequency-based sensitivity-updating algorithm is applied to identify the target cable system the proposed method is also able to identify the flexural rigidity. the axial rigidity, and the torsion rigidity of a cable. For a field application, a vibration test on hangers of the Yong Jong Grand Suspension Bridge is carried out and the collected data is used to verify the proposed method.

• KDI Journal of Economic Policy
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• v.37 no.sup
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• pp.33-63
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• 2015

This paper attempts to investigate the Korean households' inflation expectations with particular attention to information rigidity. For this purpose, we derive an empirical model from a sticky information model $\acute{a}$ la Mankiw and Reis (2002) and estimate it. In addition, it is also examined whether the expectation formation is state-dependent on macroeconomic conditions. The main findings of this paper are as follows. First, it turns out that the information rigidity in Korean households' inflation expectations is very high. In a month, most of the households simply keep their inflation expectations the same as before instead of updating them based on newly arrived information. Furthermore, when updating their expectations, the households tend to rely on the backward-looking information such as actual inflation rates in the past rather than on the forward-looking forecasts by experts. Second, it is found that the expectation formation is varying as inflation rate changes. Specifically, when the inflation is high, the sensitivity of the households' inflation expectations to actual inflation increases and the gap between inflation expectations and actual inflation shrinks. It implies that Korean households update their expectations more frequently when the inflation matters than not.

• Geophysics and Geophysical Exploration
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• v.17 no.2
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• pp.95-103
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• 2014

This paper presents a practical inversion method for recovering a three-dimensional (3D) resistivity model and static shifts simultaneously. Although this method is based on a Gauss-Newton approach that requires a sensitivity matrix, the computer time can be greatly reduced by implementing a simple and effective procedure for updating the sensitivity matrix using the Broyden's algorithm. In this research, we examine the approximate inversion procedure and the weighting factor ${\beta}$ for static shifts through inversion experiments using synthetic MT data. In methods using the full sensitivity matrix constructed only once in the iteration process, a procedure using the full sensitivity in the earlier stage is useful to produce the smallest rms data misfit. The choice of ${\beta}$ is not critical below some threshold value. Synthetic examples demonstrate that the method proposed in this paper is effective in reconstructing a 3D resistivity structure from static-shifted MT data.

• Structural Monitoring and Maintenance
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• v.2 no.1
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• pp.77-93
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• 2015

The idea of using measured dynamic characteristics for damage detection is attractive because it allows for a global evaluation of the structural health and condition. However, vibration-based damage detection for complex structures such as long-span cable-supported bridges still remains a challenge. As a suspension or cable-stayed bridge involves in general thousands of structural components, the conventional damage detection methods based on model updating and/or parameter identification might result in ill-conditioning and non-uniqueness in the solution of inverse problems. Alternatively, methods that utilize, to the utmost extent, information from forward problems and avoid direct solution to inverse problems would be more suitable for vibration-based damage detection of long-span cable-supported bridges. The auto-associative neural network (ANN) technique and the probabilistic neural network (PNN) technique, that both eschew inverse problems, have been proposed for identifying and locating damage in suspension and cable-stayed bridges. Without the help of a structural model, ANNs with appropriate configuration can be trained using only the measured modal frequencies from healthy structure under varying environmental conditions, and a new set of modal frequency data acquired from an unknown state of the structure is then fed into the trained ANNs for damage presence identification. With the help of a structural model, PNNs can be configured using the relative changes of modal frequencies before and after damage by assuming damage at different locations, and then the measured modal frequencies from the structure can be presented to locate the damage. However, such formulated ANNs and PNNs may still be incompetent to identify damage occurring at the deck members of a cable-supported bridge because of very low modal sensitivity to the damage. The present study endeavors to enhance the damage identification capability of ANNs and PNNs when being applied for identification of damage incurred at deck members. Effort is first made to construct combined modal parameters which are synthesized from measured modal frequencies and modal shape components to train ANNs for damage alarming. With the purpose of improving identification accuracy, effort is then made to configure PNNs for damage localization by adapting the smoothing parameter in the Bayesian classifier to different values for different pattern classes. The performance of the ANNs with their input being modal frequencies and the combined modal parameters respectively and the PNNs with constant and adaptive smoothing parameters respectively is evaluated through simulation studies of identifying damage inflicted on different deck members of the double-deck suspension Tsing Ma Bridge.